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Journal articles on the topic 'Baseline correction'

Author: Grafiati

Published: 4 June 2021

Last updated: 20 June 2025

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1

Liland, Kristian Hovde, Elling-Olav Rukke, Elisabeth Fjærvoll Olsen, and Tomas Isaksson. "Customized baseline correction." Chemometrics and Intelligent Laboratory Systems 109, no. 1 (2011): 51–56. http://dx.doi.org/10.1016/j.chemolab.2011.07.005.

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2

Jia, Er Hui, Bin Li, and Tao Zhang. "A Novel Baseline Correction Algorithm." Applied Mechanics and Materials 220-223 (November 2012): 2248–52. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.2248.

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This paper studies baseline correction algorithms for subtracting the background of real-word signal. A novel baseline correction algorithm is proposed that can be solved by random signal processing. With respect to generalized statistical features of the raw data, an appropriate threshold of standard deviation is set to extract the true baseline points unfailingly. Under the generalized meaning, the background at one signal point is substituted by the statistical features of its local window. By using this proposed algorithm, we established a time varying signal baseline independently and accurately. And performance evaluation shows that the proposed algorithm is more elaborate and tolerant of real-word data than the previous ones.

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3

Górski, Łukasz, Filip Ciepiela, and Małgorzata Jakubowska. "Automatic baseline correction in voltammetry." Electrochimica Acta 136 (August 2014): 195–203. http://dx.doi.org/10.1016/j.electacta.2014.05.076.

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4

Mckinnon, G. C., C. Burger, and P. Boesiger. "Spectral baseline correction using clean." Magnetic Resonance in Medicine 13, no. 1 (1990): 145–49. http://dx.doi.org/10.1002/mrm.1910130113.

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5

Chen, Yunliang, and Liankui Dai. "An Automated Baseline Correction Method Based on Iterative Morphological Operations." Applied Spectroscopy 72, no. 5 (2018): 731–39. http://dx.doi.org/10.1177/0003702817752371.

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Raman spectra usually suffer from baseline drift caused by fluorescence or other reasons. Therefore, baseline correction is a necessary and crucial step that must be performed before subsequent processing and analysis of Raman spectra. An automated baseline correction method based on iterative morphological operations is proposed in this work. The method can adaptively determine the structuring element first and then gradually remove the spectral peaks during iteration to get an estimated baseline. Experiments on simulated data and real-world Raman data show that the proposed method is accurate, fast, and flexible for handling different kinds of baselines in various practical situations. The comparison of the proposed method with some state-of-the-art baseline correction methods demonstrates its advantages over the existing methods in terms of accuracy, adaptability, and flexibility. Although only Raman spectra are investigated in this paper, the proposed method is hopefully to be used for the baseline correction of other analytical instrumental signals, such as IR spectra and chromatograms.

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Lo, Pei-Chen, and Jenq-Shiun Leu. "Adaptive Baseline Correction of Meditation EEG." American Journal of Electroneurodiagnostic Technology 41, no. 2 (2001): 142–55. http://dx.doi.org/10.1080/1086508x.2001.11079338.

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7

Liu, Juwhan, and Jack L. Koenig. "A New Baseline Correction Algorithm Using Objective Criteria." Applied Spectroscopy 41, no. 3 (1987): 447–49. http://dx.doi.org/10.1366/0003702874449110.

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A baseline correction algorithm using a least-squares procedure is developed. Linear or quadratic types of baselines are obtained through successive fitting and rejection of data points on a statistical basis. After the entire spectrum or a subsection is fitted to a least-squares line, the standard error of estimate is utilized as a criterion to determine if the fluctuation of each data point about the line can be thought of as the baseline fluctuation. Comments on various baseline correction procedures are also made.

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8

Tian, Shengze, Paolo Gardoni, Han Li, and Wancheng Yuan. "Baseline correction of ground motions with physics-based correction patterns." Geophysical Journal International 217, no. 1 (2019): 668–81. http://dx.doi.org/10.1093/gji/ggz039.

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Pan, Chao, Ruifu Zhang, Hao Luo, and Hua Shen. "Target-based algorithm for baseline correction of inconsistent vibration signals." Journal of Vibration and Control 24, no. 12 (2017): 2562–75. http://dx.doi.org/10.1177/1077546316689014.

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A target-based baseline correction algorithm that can directly assign acceleration/velocity/displacement values equal or close to preset target values is proposed in this study for correcting vibration acceleration signals with inconsistent initial velocities and displacements. Baseline shift or drift phenomena can arise for velocity and displacement values obtained by numerical integration of recorded acceleration signals using assumed initial values that are inconsistent with reality. Two indicators, the drift ratio and amplitude ratio, are also proposed to identify the degree of baseline drift and to evaluate the results of baseline correction. The traditional polynomial detrending algorithm has typically been used to remove unreasonable trends in time series, but insufficient attention has been paid to consistency between acceleration, velocity, and displacement, which is explicitly considered in the proposed algorithm. The target-based algorithm has two implementation schemes, that is, a precise scheme and a detrending scheme, which can be selected according to the degree of baseline drift. Three inconsistent vibration signals are considered to verify the validity of the proposed algorithm. The baseline shift or drift trends can be removed effectively using the proposed target-based algorithm with the drift ratio and amplitude ratio reduced to satisfactory ranges whereas the traditional method fails to correct these signals.

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10

Yang, Guofeng, Jiacai Dai, Xiangjun Liu, Meng Chen, and Xiaolong Wu. "Multiple Constrained Reweighted Penalized Least Squares for Spectral Baseline Correction." Applied Spectroscopy 74, no. 12 (2020): 1443–51. http://dx.doi.org/10.1177/0003702819885002.

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Baseline drift occurs in various measured spectra, and the existence of a baseline signal will influence qualitative and quantitative analyses. Therefore, it is necessary to perform baseline correction or background elimination before spectral analysis. In this paper, a multiple constrained asymmetric least squares method based on the penalized least squares principle is proposed for baseline correction. The method takes both baseline and peak characteristics into account. Based on the prior knowledge that the left and right boundaries of characteristic peaks should be symmetrical, additional constraints of penalized least squares are added, which ensure the symmetry of spectra. The experimental results of the proposed method on simulated spectra are compared with existing baseline correction methods to verify the accuracy and adaptability of the proposed method. The method is also successfully applied to the baseline correction of real spectra. The results show that it can be effective for estimating the baseline. In addition, this method can also be applied to the baseline correction of other similar spectral signals.

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11

Xu, Yunnan, Pang Du, Ryan Senger, John Robertson, and James L. Pirkle. "ISREA: An Efficient Peak-Preserving Baseline Correction Algorithm for Raman Spectra." Applied Spectroscopy 75, no. 1 (2020): 34–45. http://dx.doi.org/10.1177/0003702820955245.

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A critical step in Raman spectroscopy is baseline correction. This procedure eliminates the background signals generated by residual Rayleigh scattering or fluorescence. Baseline correction procedures relying on asymmetric loss functions have been employed recently. They operate with a reduced penalty on positive spectral deviations that essentially push down the baseline estimates from invading Raman peak areas. However, their coupling with polynomial fitting may not be suitable over the whole spectral domain and can yield inconsistent baselines. Their requirement of the specification of a threshold and the non-convexity of the corresponding objective function further complicates the computation. Learning from their pros and cons, we have developed a novel baseline correction procedure called the iterative smoothing-splines with root error adjustment (ISREA) that has three distinct advantages. First, ISREA uses smoothing splines to estimate the baseline that are more flexible than polynomials and capable of capturing complicated trends over the whole spectral domain. Second, ISREA mimics the asymmetric square root loss and removes the need of a threshold. Finally, ISREA avoids the direct optimization of a non-convex loss function by iteratively updating prediction errors and refitting baselines. Through our extensive numerical experiments on a wide variety of spectra including simulated spectra, mineral spectra, and dialysate spectra, we show that ISREA is simple, fast, and can yield consistent and accurate baselines that preserve all the meaningful Raman peaks.

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12

Ye, Siqi, Shao-Ping Lu, and Adrian Munteanu. "Color correction for large-baseline multiview video." Signal Processing: Image Communication 53 (April 2017): 40–50. http://dx.doi.org/10.1016/j.image.2017.01.004.

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13

Zhang, Joanne, Qianyu Dang, and Marek Malik. "Baseline Correction in Parallel Thorough QT Studies." Drug Safety 36, no. 6 (2013): 441–53. http://dx.doi.org/10.1007/s40264-013-0040-z.

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14

Miao, Shengfa, Eddy Koenders, and Arno Knobbe. "Automatic baseline correction of strain gauge signals." Structural Control and Health Monitoring 22, no. 1 (2014): 36–49. http://dx.doi.org/10.1002/stc.1658.

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15

Li, Haoran, Jisheng Dai, Tianhong Pan, Chunqi Chang, and Hing Cheung So. "Sparse Bayesian learning approach for baseline correction." Chemometrics and Intelligent Laboratory Systems 204 (September 2020): 104088. http://dx.doi.org/10.1016/j.chemolab.2020.104088.

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16

von Specht, Sebastian. "ICBM—Integrated Combined Baseline Modification: An Algorithm for Segmented Baseline Estimation." Seismological Research Letters 91, no. 1 (2019): 475–87. http://dx.doi.org/10.1785/0220190134.

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Abstract Accelerograms are the primary source for characterizing strong ground motion. It is therefore of paramount interest to have high‐quality recordings free from any nonphysical contamination. Frequently, accelerograms are affected by baseline jumps and drifts, either related to the instrument and/or a major earthquake. In this work, I propose a correction method for these undesired baseline drifts based on segmented linear least squares. The algorithm operates on the integrated waveforms and combines all three instrument components to estimate a model that modifies the baseline to be at zero continuously. The procedure consists of two steps: first a suite of models with variable numbers of discontinuities is derived for all three instrument components. During this process, the number of discontinuities is reduced in a parsimonious way, for example, two very close discontinuities are merged into a single one. In the second step, the optimal model is selected on the basis of the Bayesian information criterion. I exemplify the application on synthetic waveforms with known discontinuities and on observed waveforms from a unified strong‐motion database of the Japan Meteorological Agency (JMA) and the National Research Institute for Earth Science and Disaster Prevention (NIED, Japan) networks for the major events of the 2016 Kumamoto earthquakes. After the baseline jump correction, the waveforms are furthermore corrected for displacement according to Wang et al. (2011). The resulting displacements are comparable to the Interferometric Synthetic Aperture Radar‐derived displacement estimates for the Kumamoto earthquake sequence.

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17

Martel, Cameron, Mohsen Mosleh, Dean Eckles, and David G. Rand. "Promoting engagement with social fact-checks online: Investigating the roles of social connection and shared partisanship." PLOS ONE 20, no. 3 (2025): e0319336. https://doi.org/10.1371/journal.pone.0319336.

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Social corrections – where users correct each other – can help rectify inaccurate beliefs. However, social corrections are often ignored. Here we ask under what conditions social corrections promote engagement from corrected users, allowing for greater insight into how users respond to debunking messages (even if such responses are negative). Prior work suggests two key factors may help promote engagement with corrections – partisan alignment between users, and social connections between users. We investigate these factors here. First, we conducted a field experiment on Twitter (X) using human-looking bots to examine how shared partisanship and prior social connection affect correction engagement. We randomized whether our accounts identified as Democrat or Republican, and whether they followed Twitter users and liked three of their tweets before correcting them (creating a minimal social connection). We found that shared partisanship had no significant effect in the baseline (no social connection) condition. Interestingly, social connection increased engagement with corrections from co-partisans. Effects in the social counter-partisan condition were ambiguous. Follow-up survey experiments largely replicated these results and found evidence for a generalized norm of responding, wherein people feel more obligated to respond to people who follow them – even outside the context of misinformation correction. Our findings have important implications for increasing engagement with social corrections online.

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18

Alme, J., T. Alt, C. Andrei, et al. "Correction of the baseline fluctuations in the GEM-based ALICE TPC." Journal of Instrumentation 18, no. 11 (2023): P11021. http://dx.doi.org/10.1088/1748-0221/18/11/p11021.

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Abstract To operate the ALICE Time Projection Chamber in continuous mode during the Run 3 and Run 4 data-taking periods of the Large Hadron Collider, the multi-wire proportional chamber-based readout was replaced with gas-electron multipliers. As expected, the detector performance is affected by the so-called common-mode effect, which leads to significant baseline fluctuations. A detailed study of the pulse shape with the new readout has revealed that it is also affected by ion tails. Since reconstruction and data compression are performed fully online, these effects must be corrected at the hardware level in the FPGA-based common readout units. The characteristics of the common-mode effect and of the ion tail, as well as the algorithms developed for their online correction, are described in this paper. The common-mode dependencies are studied using machine-learning techniques. Toy Monte Carlo simulations are performed to illustrate the importance of online corrections and to investigate the performance of the developed algorithms.

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19

Guo, Shuxia, Thomas Bocklitz, and Jürgen Popp. "Optimization of Raman-spectrum baseline correction in biological application." Analyst 141, no. 8 (2016): 2396–404. http://dx.doi.org/10.1039/c6an00041j.

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20

Liu, Yuanjie. "Adversarial nets for baseline correction in spectra processing." Chemometrics and Intelligent Laboratory Systems 213 (June 2021): 104317. http://dx.doi.org/10.1016/j.chemolab.2021.104317.

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21

Gaowei, PAN, SHI Jinfang, QIU Rong, et al. "Baseline correction algorithm for laser-induced breakdown spectroscopy." Journal of Applied Optics 43, no. 3 (2022): 538–43. http://dx.doi.org/10.5768/jao202243.0307002.

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22

Chiu, Hung-Chie. "Stable baseline correction of digital strong-motion data." Bulletin of the Seismological Society of America 87, no. 4 (1997): 932–44. http://dx.doi.org/10.1785/bssa0870040932.

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Abstract Most baseline errors of analog strong-motion data still exist in highresolution data. In this study, we identify the major baseline errors of digital strong-motion data and propose a three-step algorithm to correct these errors. The major baseline errors found in these digital data consist of constant drift in the acceleration, low-frequency instrument noise, low-frequency background noise, the small initial values for acceleration and velocity, and manipulation errors. This threestep algorithm includes fitting the baseline of acceleration by the least squares, applying a high-pass filter in acceleration, and subtracting the initial values in velocity. A least-squares fit of a straight line before filtering can effectively remove the baseline drift in acceleration. Then, the filtering removes the linear trend and other low-frequency errors that exist in the acceleration. Finally, the subtracting of the initial velocity removes the linear trend of displacement. Among these three steps, only the filtering in the second step may introduce a side effect. Compared to the Volume II routine developed by Trifunac and Lee (1973), this three-step processing significantly reduces computational efforts and side effects resulting from unnecessary manipulation of data. This algorithm has been successfully tested on several types of digital strong-motion data. Several independent validations show that the proposed algorithm is stable.

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23

Weakley, Andrew T., D. Eric Aston, and Peter R. Griffiths. "Automatic Baseline Correction of Vibrational Circular Dichroism Spectra." Applied Spectroscopy 67, no. 10 (2013): 1117–26. http://dx.doi.org/10.1366/13-07078.

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24

Liu, Hai, Zhaoli Zhang, Sanya Liu, Luxin Yan, Tingting Liu, and Tianxu Zhang. "Joint Baseline-Correction and Denoising for Raman Spectra." Applied Spectroscopy 69, no. 9 (2015): 1013–22. http://dx.doi.org/10.1366/14-07760.

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25

Guorui, Hu. "Review on Baseline Correction of Strong-Motion Accelerogram." International Journal of Science, Technology and Society 3, no. 6 (2015): 309. http://dx.doi.org/10.11648/j.ijsts.20150306.16.

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26

Caron, Lambert, and Erik R. Ivins. "A baseline Antarctic GIA correction for space gravimetry." Earth and Planetary Science Letters 531 (February 2020): 115957. http://dx.doi.org/10.1016/j.epsl.2019.115957.

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27

Peng, Jiangtao, Silong Peng, An Jiang, Jiping Wei, Changwen Li, and Jie Tan. "Asymmetric least squares for multiple spectra baseline correction." Analytica Chimica Acta 683, no. 1 (2010): 63–68. http://dx.doi.org/10.1016/j.aca.2010.08.033.

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28

Liu, Xinbo, Zhimin Zhang, Pedro F. M. Sousa, et al. "Selective iteratively reweighted quantile regression for baseline correction." Analytical and Bioanalytical Chemistry 406, no. 7 (2014): 1985–98. http://dx.doi.org/10.1007/s00216-013-7610-x.

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29

Zhang, Xiaohong, Mingkui Wu, and Wanke Liu. "Receiver Time Misalignment Correction for GPS-based Attitude Determination." Journal of Navigation 68, no. 4 (2015): 646–64. http://dx.doi.org/10.1017/s0373463315000053.

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A prerequisite for a Global Positioning System (GPS) attitude determination is to calculate baselines between antennae with accuracy at the millimetre level simultaneously. However, in order to have a low cost attitude determination system, a set of Commercial-Off-The-Shelf (COTS) receivers with separate clocks are used. In this case, if the receiver clocks are not precisely synchronized, the baseline vector between antennae will be calculated from the GPS signals received at different times. This can be a significant error source for high-kinematic applications. In this paper, two equivalent and effective approaches are developed to compensate this significant bias for baseline estimation and attitude determination. Test results using real airborne GPS data demonstrate that the receiver time misalignment between the two receivers can result in a 5 cm baseline offset for an aircraft with a 50 m/s velocity; the corresponding attitude errors can reach about 0·50° in yaw and 0·10° in pitch respectively for the attitude determination system with a baseline length of 3·79 m. With the proposed methods, these errors can be effectively eliminated.

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30

Shalini, Wincent Anto Win, Thulasi Rajalakshmi, and Selvanayagam Vasanthadev Suryakala. "Refining thyroid function evaluation: a comparative study of preprocessing methods in diffuse reflectance spectroscopy." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 1 (2025): 303–10. https://doi.org/10.11591/ijece.v15i1.pp303-310.

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Thyroid dysfunction, comprising conditions such as hyperthyroidism and hypothyroidism, represents a substantial global health challenge, necessitating timely and precise diagnosis for effective therapeutic intervention and patient welfare. Conventional diagnostic modalities often involve invasive procedures, that could cause discomfort and inconvenience for individuals. The non-invasive techniques like diffuse reflectance spectroscopy (DRS) can offer a promising alternative. This study underscores the critical role of preprocessing methods in enhancing the accuracy of thyroid hormone functionality through a non-invasive approach. In the proposed study the spectral data acquired from the DRS setup are subjected to different preprocessing techniques to improve the efficacy of the prediction model. Thirty individuals with thyroid dysfunction were included in the study, and preprocessing methods such as baseline correction, multiplicative scatter correction (MSC), and standard normal variate (SNV), were systematically evaluated. The study highlights that SNV preprocessing outperformed other methods with a root mean square error (RMSE) of 0.005 and an R² of 0.99. In contrast, MSC resulted in an RMSE of 0.87 and an R² of 0.86, while baseline correction showed a RMSE of 0.84 and an unusual R² of 1.09, indicating potential issues. SNV proved to be the most effective technique.

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31

Peng, Xiao Bo, Xiao Jun Peng, and Wei Lin Yang. "Near-Fault Baseline Correction Method and Appliction in Wenchuan Earthquake." Applied Mechanics and Materials 166-169 (May 2012): 2517–21. http://dx.doi.org/10.4028/www.scientific.net/amm.166-169.2517.

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To eliminate the drifting in recordings from Ms8.0 Wenchuan earthquake, a modified two segments baseline correction method is brought up and systematic acceptance standard is presented. The method is applied to recordings from Qingping station and Bajiao station, which are closest stations to surface rupture traces. The effect of baseline correction on acceleration time history, Fourier amplitude spectrum and 5% damped relative displacement response spectrum is analyzed.

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32

Ghasemi Falavarjani, Khalil, Reza Mirshahi, Shahriar Ghasemizadeh, and Mahsa Sardarinia. "Stepwise segmentation error correction in optical coherence tomography angiography images of patients with diabetic macular edema." Therapeutic Advances in Ophthalmology 12 (January 2020): 251584142094793. http://dx.doi.org/10.1177/2515841420947931.

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Aim: To determine the minimum number of optical coherence tomography B-scan corrections required to provide acceptable vessel density measurements on optical coherence tomography angiography images in eyes with diabetic macular edema. Methods: In this prospective, noninterventional case series, the optical coherence tomography angiography images of eyes with center-involving diabetic macular edema were assessed. Optical coherence tomography angiography imaging was performed using RTVue Avanti spectral-domain optical coherence tomography system with the AngioVue software (V.2017.1.0.151; Optovue, Fremont, CA, USA). Segmentation error was recorded and manually corrected in the inner retinal layers in the central foveal, 100th and 200th optical coherence tomography B-scans. The segmentation error correction was then continued until all optical coherence tomography B-scans in whole en face image were corrected. At each step, the manual correction of each optical coherence tomography B-scan was propagated to whole image. The vessel density and retinal thickness were recorded at baseline and after each optical coherence tomography B-scan correction. Results: A total of 36 eyes of 26 patients were included. To achieve full segmentation error correction in whole en face image, an average of 1.72 ± 1.81 and 5.57 ± 3.87 B-scans was corrected in inner plexiform layer and outer plexiform layer, respectively. The change in the vessel density measurements after complete segmentation error correction was statistically significant after inner plexiform layer correction. However, no statistically significant change in vessel density was found after manual correction of the outer plexiform layer. The vessel density measurements were statistically significantly different after single central foveal B-scan correction of inner plexiform layer compared with the baseline measurements ( p = 0.03); however, it remained unchanged after further segmentation corrections of inner plexiform layer. Conclusion: Multiple optical coherence tomography B-scans should be manually corrected to address segmentation error in whole images of en face optical coherence tomography angiography in eyes with diabetic macular edema. Correction of central foveal B-scan provides the most significant change in vessel density measurements in eyes with diabetic macular edema.

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33

Baek, Sung-June, Aaron Park, Young-Jin Ahn, and Jaebum Choo. "Baseline correction using asymmetrically reweighted penalized least squares smoothing." Analyst 140, no. 1 (2015): 250–57. http://dx.doi.org/10.1039/c4an01061b.

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34

Sasatake, Yuta, and Kojiro Matsushita. "EEG Baseline Analysis for Effective Extraction of P300 Event-Related Potentials for Welfare Interfaces." Sensors 25, no. 10 (2025): 3102. https://doi.org/10.3390/s25103102.

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Enabling individuals with complete paralysis to operate devices voluntarily requires an effective interface; EEG-based P300 event-related potential (ERP) interfaces are considered a promising approach. P300 is an EEG peak generated in response to specific sensory stimuli recognized by an individual. Accurate detection of this peak necessitates a stable pre-stimulus baseline EEG signal, which serves as the reference for baseline correction. Previous studies have commonly employed either a single-time-point amplitude (e.g., at 100 ms before stimulus onset) or a time-range-averaged amplitude over a specified pre-stimulus period (e.g., 0–200 ms) as a baseline correction method, assuming these provide the most stable EEG reference. However, in assistive P300 interfaces, continuous visual stimuli at 400 ms intervals are typically used to efficiently evoke P300 peaks. Since stimuli are presented before the EEG stabilizes, it remains unclear whether conventional neuroscience baseline correction methods are suitable for such applications. To address this, the present study conducted a P300 induction experiment based on continuous 400 ms interval visual stimuli. Using EEG data recorded from 0 to 1000 ms before each visual stimulus (sampled at 1 ms intervals), we applied three baseline correction methods—single-time-point amplitude, time-range-averaged amplitude, and multi-time-point amplitude—to determine the most effective EEG reference and evaluate the impact on P300 detection performance. The results showed that baseline correction using an amplitude at a single point in time is unstable when the basic EEG rhythm and low-frequency noise remain, while time-range-averaged baseline correction using the 0–200 ms pre-stimulus period led to relatively effective P300 detection. However, it was also found that using only one value averaged over the amplitude from 0 to 200 ms did not result in an accurate EEG reference potential, resulting in an error. Finally, this study confirmed that the multi-time-point baseline correction method, through which the amplitude state from 0 to 200 ms before the visual stimulus is comprehensively evaluated, may be the most effective method for P300 determination.

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35

Chen, Zengshun, Jun Fu, Yanjian Peng, Tuanhai Chen, LiKai Zhang, and Chenfeng Yuan. "Baseline Correction of Acceleration Data Based on a Hybrid EMD–DNN Method." Sensors 21, no. 18 (2021): 6283. http://dx.doi.org/10.3390/s21186283.

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Measuring displacement response is essential in the field of structural health monitoring and seismic engineering. Numerical integration of the acceleration signal is a common measurement method of displacement data. However, due to the circumstances of ground tilt, low-frequency noise caused by instruments, hysteresis of the transducer, etc., it would generate a baseline drift phenomenon in acceleration integration, failing to obtain an actual displacement response. The improved traditional baseline correction methods still have some problems, such as high baseline correction error, poor adaptability, and narrow application scope. This paper proposes a deep neural network model based on empirical mode decomposition (EMD–DNN) to solve baseline correction by removing the drifting trend. The feature of multiple time sequences that EMD obtains is extracted via DNN, achieving the real displacement time history of prediction. In order to verify the effectiveness of the proposed method, two natural waves (EL centro wave, Taft wave) and one Artificial wave are selected to test in a shaking table test. Comparing the traditional methods such as the least squares method, EMD, and DNN method, EMD–DNN has the best baseline correction effect in terms of the evaluation indexes: Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and degree of fit (R-Square).

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36

Gkountanas, Kostas, Ioanna Dagla, Evangelos Gikas, Anđelija Malenović, and Yannis Dotsikas. "Baseline Correction for HPLC Chromatograms by Using Free Open-Source Software." Analytica 4, no. 1 (2023): 45–53. http://dx.doi.org/10.3390/analytica4010005.

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Chromatograms with overlapping peaks and a baseline rise or upset constitute a great challenge for analysts. Such a case regarding the analysis of bupropion hydrochloride and its 5 impurities in a tablet formulation was used as a model. A baseline correction technique for liquid chromatography coupled with diode array detection is described by using Rstudio. The asymmetry least squares (ALS) algorithm was used as implemented in the “baseline” package, with parameters lambda and p set to 4 and 0.05, respectively. Peak deconvolution and subsequent integration and area quantification were accomplished through Fytik software. Chromatographic data from the validation procedure were utilized to demonstrate the feasibility of the suggested method and whether this correction affects the outcome of the validation study. Finally, a robustness study was carried out in order to shed light on the factors that have a more significant influence on the baseline correction, showing the reliability of this procedure through random changes in its parameters.

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37

YAN Hongyu, 闫红宇, 赵宇 ZHAO Yu, 陈媛媛 CHEN Yuanyuan та ін. "激光诱导击穿光谱的自适应基线校正方法". ACTA PHOTONICA SINICA 53, № 6 (2024): 0630001. http://dx.doi.org/10.3788/gzxb20245306.0630001.

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38

Eliasberg, Claire D., Kyle N. Kunze, Erica Swartwout, Atul F. Kamath, Hugo Robichaud, and Anil S. Ranawat. "Extreme Hinge Axis Positions Are Necessary to Achieve Posterior Tibial Slope Reduction With Small Coronal-Plane Corrections in Medial Opening Wedge High Tibial Osteotomy." Orthopaedic Journal of Sports Medicine 10, no. 5 (2022): 232596712210943. http://dx.doi.org/10.1177/23259671221094346.

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Background: Both coronal- and sagittal-plane knee malalignment can increase the risk of ligamentous injuries and the progression of degenerative joint disease. High tibial osteotomy can achieve multiplanar correction, but determining the precise hinge axis position for osteotomy is technically challenging. Purpose: To create computed tomography (CT)–based patient-specific models to identify the ideal hinge axis position angle and the amount of maximum opening in medial opening wedge high tibial osteotomy (MOWHTO) required to achieve the desired multiplanar correction. Study Design: Descriptive laboratory study. Methods: A total of 10 patients with lower extremity CT scans were included. Baseline measurements including the mechanical tibiofemoral angle (mTFA) and the posterior tibial slope (PTS) were calculated. Virtual osteotomy was performed to achieve (1) a specified degree of PTS correction and (2) a planned degree of mTFA correction. The mean hinge axis position angle for MOWHTO to maintain an anatomic PTS (no slope correction) was 102.6° ± 8.3° relative to the posterior condylar axis (PCA). Using this as the baseline correction, the resultant hinge axis position and maximum opening were then calculated for each subsequent osteotomy procedure. Results: For 5.0° of mTFA correction, the hinge axis position was decreased by 6.8°, and the maximum opening was increased by 0.49 mm for every 1° of PTS correction. For 10.0° of mTFA correction, the hinge axis position was decreased by 5.2°, and the maximum opening was increased by 0.37 mm for every 1° of PTS correction. There was a significant difference in the trend-line slopes for hinge axis position versus PTS correction ( P = .013) and a significant difference in the trend-line intercepts for maximum opening versus PTS correction ( P < .0001). Conclusion: The mean hinge axis position for slope-neutral osteotomy was 102.6° ± 8.3° relative to the PCA. For smaller corrections in the coronal plane, more extreme hinge axis positions were necessary to achieve higher magnitudes of PTS reduction. Clinical Relevance: Extreme hinge axis positions are technically challenging and can lead to unstable osteotomy. Patient-specific instrumentation may allow for precise correction to be more readily achieved.

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Schatz, Philip, Rosemarie Scolaro Moser, Gary S. Solomon, Summer D. Ott, and Robin Karpf. "Prevalence of Invalid Computerized Baseline Neurocognitive Test Results in High School and Collegiate Athletes." Journal of Athletic Training 47, no. 3 (2012): 289–96. http://dx.doi.org/10.4085/1062-6050-47.3.14.

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Context: Limited data are available regarding the prevalence and nature of invalid computerized baseline neurocognitive test data. Objective: To identify the prevalence of invalid baselines on the desktop and online versions of ImPACT and to document the utility of correcting for left-right (L-R) confusion on the desktop version of ImPACT. Design: Cross-sectional study of independent samples of high school (HS) and collegiate athletes who completed the desktop or online versions of ImPACT. Participants or Other Participants: A total of 3769 HS (desktop = 1617, online = 2152) and 2130 collegiate (desktop = 742, online = 1388) athletes completed preseason baseline assessments. Main Outcome Measure(s): Prevalence of 5 ImPACT validity indicators, with correction for L-R confusion (reversing left and right mouse-click responses) on the desktop version, by test version and group. Chi-square analyses were conducted for sex and attentional or learning disorders. Results: At least 1 invalid indicator was present on 11.9% (desktop) versus 6.3% (online) of the HS baselines and 10.2% (desktop) versus 4.1% (online) of collegiate baselines; correcting for L-R confusion (desktop) decreased this overall prevalence to 8.4% (HS) and 7.5% (collegiate). Online Impulse Control scores alone yielded 0.4% (HS) and 0.9% (collegiate) invalid baselines, compared with 9.0% (HS) and 5.4% (collegiate) on the desktop version; correcting for L-R confusion (desktop) decreased the prevalence of invalid Impulse Control scores to 5.4% (HS) and 2.6% (collegiate). Male athletes and HS athletes with attention deficit or learning disorders who took the online version were more likely to have at least 1 invalid indicator. Utility of additional invalidity indicators is reported. Conclusions: The online ImPACT version appeared to yield fewer invalid baseline results than did the desktop version. Identification of L-R confusion reduces the prevalence of invalid baselines (desktop only) and the potency of Impulse Control as a validity indicator. We advise test administrators to be vigilant in identifying invalid baseline results as part of routine concussion management and prevention programs.

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Chiu, Hung-Chie. "A Compatible Baseline Correction Algorithm for Strong-Motion Data." Terrestrial, Atmospheric and Oceanic Sciences 23, no. 2 (2012): 171. http://dx.doi.org/10.3319/tao.2011.10.25.01(t).

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Ye Song, 叶. 松., 甘永莹 Gan Yongying, 熊. 伟. Xiong Wei, 张文涛 Zhang Wentao, 汪杰君 Wang Jiejun, and 王新强 Wang Xinqiang. "Baseline correction of spatial heterodyne spectrometer using wavelet transform." Infrared and Laser Engineering 45, no. 11 (2016): 1117009. http://dx.doi.org/10.3788/irla201645.1117009.

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Gibson, Rebecca L., Mark J. H. Simmons, E. Hugh Stitt, Lockhart Horsburgh, and Robert W. Gallen. "Selection of Formal Baseline Correction Methods in Thermal Analysis." Chemical Engineering & Technology 45, no. 2 (2021): 238–48. http://dx.doi.org/10.1002/ceat.202100120.

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Ye Song, 叶. 松., 甘永莹 Gan Yongying, 熊. 伟. Xiong Wei, 张文涛 Zhang Wentao, 汪杰君 Wang Jiejun, and 王新强 Wang Xinqiang. "Baseline correction of spatial heterodyne spectrometer using wavelet transform." Infrared and Laser Engineering 45, no. 11 (2016): 1117009. http://dx.doi.org/10.3788/irla20164511.1117009.

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Liu, Juntao, Jianyang Sun, Xiuzhen Huang, Guojun Li, and Binqiang Liu. "Goldindec: A Novel Algorithm for Raman Spectrum Baseline Correction." Applied Spectroscopy 69, no. 7 (2015): 834–42. http://dx.doi.org/10.1366/14-07798.

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郁, 涛. "Phase Jump of Virtual Baseline as Well as Correction." Journal of Antennas 02, no. 01 (2013): 7–11. http://dx.doi.org/10.12677/ja.2013.21002.

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Wang, Xiao, You Zhou, Minglei Shu, Yinglong Wang, and Anming Dong. "ECG Baseline Wander Correction and Denoising Based on Sparsity." IEEE Access 7 (2019): 31573–85. http://dx.doi.org/10.1109/access.2019.2902616.

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Overall, John E., and Kevin N. Magee. "Baseline correction in a two-way randomized blocks design." Journal of Biopharmaceutical Statistics 2, no. 2 (1992): 205–17. http://dx.doi.org/10.1080/10543409208835040.

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Han, Quanjie, Qiong Xie, Silong Peng, and Baokui Guo. "Simultaneous spectrum fitting and baseline correction using sparse representation." Analyst 142, no. 13 (2017): 2460–68. http://dx.doi.org/10.1039/c6an02341j.

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Chang, David, Cory D. Banack, and Sirish L. Shah. "Robust baseline correction algorithm for signal dense NMR spectra." Journal of Magnetic Resonance 187, no. 2 (2007): 288–92. http://dx.doi.org/10.1016/j.jmr.2007.05.008.

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Chiung-Shen Wu, Gin-Kou Ma, and Bao-Shuh P. Lin. "Correction To "Extended Baseline Architecture For Nonblocking Photonic Switching"." Journal of Lightwave Technology 15, no. 7 (1997): 1253. http://dx.doi.org/10.1109/jlt.1997.596972.

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